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https://github.com/farhansadaf/face-mask-detection

Real-time face mask detection.
https://github.com/farhansadaf/face-mask-detection

computer-vision haarcascade-frontalface keras mtcnn-face-detection

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Real-time face mask detection.

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# Real-time Face Mask Detection



## Overview

Face masks help reduce the transmission of SARS-CoV-2 by interfering with the spread of virus-laden droplets ejected from the nose and mouth. 

Wearing face mask is one of the precautionary steps an individual can take in order to lessen the spread of COVID-19.

In this simple project, a video camera detects if an individual is wearing a face mask or not in real-time.


The detection steps looks somewhat like this:




## Quickstart the project

1. Download the code in `ZIP` or open with `GitHub Desktop` or `git clone https://github.com/FarhanSadaf/face-mask-detection.git`. If you face issues downloading weight file, you can download from this google drive link.

2. Install required python dependencies into your python environment / virtual environment using `pip install -r requirements.txt`.

3. Run `script-1.py` for real-time detection using webcam. If detection isn't to your liking, try running `script-2.py`.



## Dataset

The dataset used to train the face-mask detection model taken from here.



This dataset contains 853 images belonging to the 3 classes, as well as their bounding boxes in the PASCAL VOC format.

The classes are:

- With mask

- Without mask

- Mask worn incorrectly






Though the dataset is pretty imbalanced with most of the data belonging to `With mask` class, it works pertty well detecting `With mask` and `Without mask` classes.

But more data for `Mask worn incorrectly` class is needed in order to detect this class properly.



## Workflow

1. Data preprocessing : 

All images have their bounding boxes in the PASCAL VOC format and their info saved in `XML` format in `annotaions` directory. 

Only the region bounded by bounding box taken as input and their respective labels taken as output.



2. Training mask detector model :

Transfer learning was used to train the inputs. The classifier model was built with InceptionV3 neural network architecture.

After training for 20 epochs, accuracy on test set was 96.81%.



3. Detecting face mask in real-time :

First task was to detect faces from each frame of the video. 

At first I used Haarcascade classifer from OpenCV for face detection. Average FPS I got while running on my machine was around 16. 

But face detection wasn't that accurate. This classifer struggled detecting faces with mask. In low-light condition it struggled the most.


Then I tried MTCNN for face detection. 

This algorithm performed great detecting faces, even in the low light. But while running on my machine, the average FPS I got was about 1.4. 

Which is pretty slow comparing with haarcascade classifier. 



## Results



Input

Output








## Links and References

- Face Mask Detection dataset: https://www.kaggle.com/andrewmvd/face-mask-detection

- InceptionV3: https://www.tensorflow.org/api_docs/python/tf/keras/applications/InceptionV3

- Face Detection using Haar Cascades: https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_objdetect/py_face_detection/py_face_detection.html

- How to Perform Face Detection with Deep Learning: https://machinelearningmastery.com/how-to-perform-face-detection-with-classical-and-deep-learning-methods-in-python-with-keras/

- GitHub MTCNN: https://github.com/ipazc/mtcnn



## Licensing

The code in this project is licensed under [MIT License](LICENSE).